AIMC Topic: Otolaryngology

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Evaluating Locally Run Large Language Models (Gemma 2, Mistral Nemo, and Llama 3) for Outpatient Otorhinolaryngology Care: Retrospective Study.

JMIR formative research
BACKGROUND: Large language models (LLMs) have great potential to improve and make the work of clinicians more efficient. Previous studies have mainly focused on web-based services, such as ChatGPT, often with simulated cases. For the processing of pe...

Automated Evaluation of Reflection and Feedback Quality in Workplace-Based Assessments by Using Natural Language Processing: Cross-Sectional Competency-Based Medical Education Study.

JMIR medical education
BACKGROUND: Competency-based medical education relies heavily on high-quality narrative reflections and feedback within workplace-based assessments. However, evaluating these narratives at scale remains a significant challenge.

[Evaluation of ChatGPT's responses to symptom-oriented questions in otolaryngology].

Orvosi hetilap
Introduction: Chat Generative Pre-Trained Transformer (ChatGPT) is a recently developed artificial intelligence (AI)-based language model that has become an increasingly common source of health-related information due to its accessibility. However, t...

Readability of custom chatbot vs. GPT-4 responses to otolaryngology-related patient questions.

American journal of otolaryngology
BACKGROUND: Low health literacy among patients hinders comprehension of care instructions and worsens outcomes, yet most otolaryngology patient materials and chatbot responses to medical inquiries exceed the recommended reading level of sixth- to eig...

Comparison of physician and large language model chatbot responses to online ear, nose, and throat inquiries.

Scientific reports
Large language models (LLMs) can potentially enhance the accessibility and quality of medical information. This study evaluates the reliability and quality of responses generated by ChatGPT-4, an LLM-driven chatbot, compared to those written by physi...

Comparative analysis of AI-generated study guides in otolaryngology education.

American journal of otolaryngology
INTRODUCTION: Resident physicians training in otolaryngology frequently utilize dense traditional textbooks like "Cummings: Otolaryngology Head and Neck Surgery," widely regarded as the gold standard for educational content in the field. However, int...

Generative AI in Otolaryngology Residency Personal Statement Writing: A Mixed-Methods Analysis.

The Laryngoscope
OBJECTIVE: Generative Artificial Intelligence (GAI) interfaces have rapidly integrated into various societal domains. Widespread accessibility of GAI for drafting personal statements poses challenges for evaluators to gauge writing ability and person...

[Structured reporting in otorhinolaryngology].

HNO
Structured reporting (SR) is a valuable method for optimizing diagnosis and treatment in various specialist disciplines. While conventional free-text findings are often inconsistent and difficult to compare, structured documentation enables higher qu...

Advancing laryngology through artificial intelligence: a comprehensive review of implementation frameworks and strategies.

Current opinion in otolaryngology & head and neck surgery
PURPOSE OF REVIEW: This review aims to explore the integration of artificial intelligence (AI) in laryngology, with specific focus on the barriers preventing translation from pilot studies into routine clinical practice and strategies for successful ...

Artificial intelligence in otorhinolaryngology: current trends and application areas.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: This study aims to perform a bibliometric analysis of scientific research on the use of artificial intelligence (AI) in the field of Otorhinolaryngology (ORL), with a specific focus on identifying emerging AI trend topics within this discipl...